BB-KBQA:BERT-Based Knowledge Base Question Answering

• 作 者: Aiting Liu Ziqi Huang Hengtong Lu Xiaojie Wang Caixia Yuan
• 机 构: Beijing University of Posts and Telecommunications,Beijing,China
• 出 处: 第十八届中国计算语言学大会暨中国中文信息学会2019学术年会
• 发表日期: 2019年8期
• 关键词: Chinese Knowledge Base Question Answering Entity Linking Predicate Mapping BERT

　　Knowledge base question answering aims to answer natural language questions by querying external knowledge base,which has been widely applied to many real-world systems.Most existing methods are template-based or training BiLSTMs or CNNs on the task-specific dataset.However,the hand-crafted templates are time-consuming to design as well as highly formalist without generalization ability.At the same time,BiLSTMs and CNNs require large-scale training data which is unpractical in most cases.To solve these problems,we utilize the prevailing pre-trained BERT model which leverages prior linguistic knowledge to obtain deep contextualized representations.Experimental results demonstrate that our model can achieve the state-of-the-art performance on the NLPCC-ICCPOL 2016 KBQA dataset,with an 84.12%averaged F1 score(1.65%absolute improvement).